Summary. I describe the background for the paper 'Controlling the false discovery rate: a new and powerful approach to multiple comparisons' by Benjamini and Hochberg that was published in the Journal of the Royal Statistical Society, Series B, in 1995. I review the progress since made on the...
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57, 289–300 (1995). Article MathSciNet Google Scholar Wagenaar, D. A., Pine, J. & Potter, S. M. An extremely rich repertoire ...
The P values obtained using the “Scaling_chi2_hapflk.py” script available at https://forge-dga.jouy.inra.fr/documents, were corrected for multiple comparisons using the false-discovery rate (FDR) method in R and SNPs (with a P value ≤ 0.05)were considered significant. Graphical ...
The false discovery rate (FDR) is quite an interesting metric in classification problems, corresponding to the proportion of events in which the null hypothesis is incorrectly rejected, or in other words, the likelihood of incurring in type I error in a test [44, 45]. In our benchmarking ...
The false discovery rate (FDR) is quite an interesting metric in classification problems, corresponding to the proportion of events in which the null hypothesis is incorrectly rejected, or in other words, the likelihood of incurring in type I error in a test [44, 45]. In our benchmarking ...
(1995) Controlling the false discovery rate: A new and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289–300. MATH MathSciNet Google Scholar Benjamini, Y., & Yekutieli, D. (2001) The control of the false discovery rate in multiple ...
-f/--fdr : false discovery rate(<0.05) -m/--max-num : maximum locations for each read [default=1] -s/--seed-len : the length of seed for searching [default>=10] -x/--mismatch : maximum mismatch number for each read [default<=1] ...
of the proportion of false positives that our algorithm will necessarily recover along with the putative predictions. The false discovery rate (FDR) is quite an interesting metric in classification problems, corresponding to the proportion of events in which the null hypothesis is incorrectly rejected,...
When given r and γ, the ROC curve plots the true-positive rate (TPR) versus the false-positive rate (FPR) subject to the threshold (p-value) separat- ing the identification results in 200 replicates. To compare different curves obtained by ROC analysis, we calculated the area under the ...